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Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management

Author

Listed:
  • Jiseong Noh

    (Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea)

  • Hyun-Ji Park

    (Graduate School of Management Consulting, Hanyang University, Erica, Ansan 15588, Korea)

  • Jong Soo Kim

    (Department of Industrial and Management Engineering, Hanyang University, Erica, Ansan 15588, Korea)

  • Seung-June Hwang

    (Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea)

Abstract

Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

Suggested Citation

  • Jiseong Noh & Hyun-Ji Park & Jong Soo Kim & Seung-June Hwang, 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management," Mathematics, MDPI, vol. 8(4), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:4:p:565-:d:344368
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    References listed on IDEAS

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    1. Jong Soo Kim & K Y Shin & S E Ahn, 2003. "A multiple replenishment contract with ARIMA demand processes," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(11), pages 1189-1197, November.
    2. Panapakidis, Ioannis P. & Dagoumas, Athanasios S., 2016. "Day-ahead electricity price forecasting via the application of artificial neural network based models," Applied Energy, Elsevier, vol. 172(C), pages 132-151.
    3. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    4. Manish Shukla & Sanjay Jharkharia, 2011. "ARIMA models to forecast demand in fresh supply chains," International Journal of Operational Research, Inderscience Enterprises Ltd, vol. 11(1), pages 1-18.
    5. Keles, Dogan & Scelle, Jonathan & Paraschiv, Florentina & Fichtner, Wolf, 2016. "Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks," Applied Energy, Elsevier, vol. 162(C), pages 218-230.
    6. Zeynep Hilal Kilimci & A. Okay Akyuz & Mitat Uysal & Selim Akyokus & M. Ozan Uysal & Berna Atak Bulbul & Mehmet Ali Ekmis, 2019. "An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain," Complexity, Hindawi, vol. 2019, pages 1-15, March.
    7. Babai, M.Z. & Ali, M.M. & Boylan, J.E. & Syntetos, A.A., 2013. "Forecasting and inventory performance in a two-stage supply chain with ARIMA(0,1,1) demand: Theory and empirical analysis," International Journal of Production Economics, Elsevier, vol. 143(2), pages 463-471.
    8. Su, Huai & Zio, Enrico & Zhang, Jinjun & Xu, Mingjing & Li, Xueyi & Zhang, Zongjie, 2019. "A hybrid hourly natural gas demand forecasting method based on the integration of wavelet transform and enhanced Deep-RNN model," Energy, Elsevier, vol. 178(C), pages 585-597.
    9. Peng, Lu & Liu, Shan & Liu, Rui & Wang, Lin, 2018. "Effective long short-term memory with differential evolution algorithm for electricity price prediction," Energy, Elsevier, vol. 162(C), pages 1301-1314.
    10. Jae Young Choi & Bumshik Lee, 2018. "Combining LSTM Network Ensemble via Adaptive Weighting for Improved Time Series Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-8, August.
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